SoftBound: highly compatible and complete spatial memory safety for c

By Santosh Nagarakatte, Jianzhou Zhao, Milo M. K. Martin, Steve Zdancewic. 2009

697 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Hardware and Architecture, Signal Processing

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Citations in the corpus

Forward Compatible Few-Shot Class-Incremental Learning (2022, methodology): “Backward Compatible Learning: The concept ‘compati-bility’ is a design characteristic considered in software engineering [17, 30, 45, 51].”
VC3: Trustworthy Data Analytics in the Cloud Using SGX (2015, background): “Since memory safety implementations for C/C++ have high overhead [43], [44], [60], we instead address this problem with a compiler that efficiently enforces two security invariants for code running inside the enclave.”
Deep Class-Incremental Learning: A Survey (2023, background): “CIL with Bidirectional Compatibility: Compatibility is a design characteristic in software engineering community [240], [241], [242], [243], which was introduced to the machine learning community in [244], [245].”
CETS: compiler enforced temporal safety for C (2010, methodology): “A shadowspace may be implemented as a large, directly accessed memory region, a hashtable [ 22 ], or a trie-based data structure [24].”
Control-Flow Integrity (2017, background): “While mechanisms that enforce spatial [45] and temporal [46] memory safety eliminate memory corruption (and thereby control-flow hijacking attacks), existing mechanisms are considered prohibitively expensive.”
Code-pointer integrity (2014, methodology): “These rules are identical to the corresponding rules of SoftBound [Nagarakatte et al. 2009] and ensure full spatial memory safety of all memory objects in the safe memory.”
SGXBOUNDS: Memory Safety for Shielded Execution (2017, methodology): “As already mentioned, we leave libc uninstrumented and introduce manually written wrappers for all libc functions, similar to other approaches [9, 17, 56, 69].”
Class-Incremental Learning: A Survey (2023, positive): “CIL with Bidirectional Compatibility : Compatibility is a design characteristic in software engineering community [229], [230], [231], [232], which was introduced to the machine learning community in [233], [234].”
The Art, Science, and Engineering of Fuzzing: A Survey (2018, background): “SoftBound/CETS [165], [166] is another memory error detector that instruments programs during compilation.”
Baggy Bounds Checking: An Efficient and Backwards-Compatible Defense against Out-of-Bounds Errors (2009, methodology): “The latest proposal, SoftBound [23], tracks bounds for each pointer to achieve sub-object protection.”
Data-Oriented Programming: On the Expressiveness of Non-control Data Attacks (2016, positive): “SoftBound with CETS stores metadata for each pointer inside a disjointed memory for bound checking and identiﬁer matching to force a complete memory safety [53], [54].”